A Sentinel-2 machine learning dataset for tree species classification in Germany
<p>We present a machine learning dataset for tree species classification in Sentinel-2 satellite image time series of bottom-of-atmosphere reflectance. It is geared towards training classifiers but is less suitable for validating the resulting maps. The dataset is based on the German National...
Saved in:
Main Authors: | M. Freudenberg, S. Schnell, P. Magdon |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2025-02-01
|
Series: | Earth System Science Data |
Online Access: | https://essd.copernicus.org/articles/17/351/2025/essd-17-351-2025.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
STURM-Flood: a curated dataset for deep learning-based flood extent mapping leveraging Sentinel-1 and Sentinel-2 imagery
by: Nicla Notarangelo, et al.
Published: (2025-02-01) -
A Comparative Analysis of Spatial Resolution Sentinel-2 and Pleiades Imagery for Mapping Urban Tree Species
by: Fabio Recanatesi, et al.
Published: (2025-01-01) -
Performance of Quantum Annealing Machine Learning Classification Models on ADMET Datasets
by: Hadi Salloum, et al.
Published: (2025-01-01) -
Optimizing SVM for argan tree classification using Sentinel-2 data: A case study in the Sous-Massa Region, Morocco
by: Abdelhak El Kharki, et al.
Published: (2024-11-01) -
Combined Sentinel-1 and Sentinel-2 Imagery for Destroyed Building Classification in Gaza Strip With Random Forest
by: Xinchen Li, et al.
Published: (2025-01-01)